Previously, we introduced a large-scale dataset (CNN Filter DB) [1]} that consists of convolution filters with a kernel size of \(3\times 3\)  extracted from hundreds of publicly available pre-trained CNNs trained for a multitude of tasks. On that data, we performed a singular-value decomposition (SVD) comparison of sets of filters aggregated by various dimensions of meta-data. Therefore, sets of normalized filters were transformed to a common SVD-obtained basis, and the shift between resulting distributions of the coefficients (\(c_{ij}\) ) was measured based on a variant of the Kullback-Leibler Divergence (KL). Generally, individual models and model families (e.g. ResNet) learn unique distributions of filters, but on average, distributions segregated by the learned task, image domain, or dataset do not significantly differ. Most observed shifts were due to different levels of “degenerated” (i.e. sparse or highly repetitive) filters caused by the over-parameterization of networks relative to the dataset.
However, the most salient outlier in shifts included medical imaging models, as this domain showed distinct “spikey” coefficient distributions. In this paper, we set our focus to study these models in detail and understand the possible causes of the shifts. The models can be summarized in the following:
CompNet [2]} contains three customized architectures trained for brain segmentation on the OASIS (MRI) dataset [3]};
LungMask [4]} contains three UNet trained for lung segmentation on the LTRC and some proprietary CT datasets;
TorchXRayVision [5]} contains seven DensetNet-121 and one ResNet50 classifier trained to detect various pathologies from various datasets. Additionally, the framework includes one ResNet101 auto-encoder trained on a large aggregated dataset consisting of the previously mentioned datasets;
Lastly, one individual UNet [6]} is included that is also trained to perform brain segmentation on a public Kaggle MRI dataset [7]}.
